Overview

Dataset statistics

Number of variables12
Number of observations1551
Missing cells0
Missing cells (%)0.0%
Duplicate rows212
Duplicate rows (%)13.7%
Total size in memory145.5 KiB
Average record size in memory96.1 B

Variable types

Numeric12

Warnings

Dataset has 212 (13.7%) duplicate rowsDuplicates
fixed acidity is highly correlated with citric acid and 2 other fieldsHigh correlation
volatile acidity is highly correlated with citric acidHigh correlation
citric acid is highly correlated with fixed acidity and 2 other fieldsHigh correlation
free sulfur dioxide is highly correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly correlated with free sulfur dioxideHigh correlation
density is highly correlated with fixed acidityHigh correlation
pH is highly correlated with fixed acidity and 1 other fieldsHigh correlation
fixed acidity is highly correlated with citric acid and 2 other fieldsHigh correlation
volatile acidity is highly correlated with citric acidHigh correlation
citric acid is highly correlated with fixed acidity and 2 other fieldsHigh correlation
free sulfur dioxide is highly correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly correlated with free sulfur dioxideHigh correlation
density is highly correlated with fixed acidityHigh correlation
pH is highly correlated with fixed acidity and 1 other fieldsHigh correlation
fixed acidity is highly correlated with pHHigh correlation
free sulfur dioxide is highly correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly correlated with free sulfur dioxideHigh correlation
pH is highly correlated with fixed acidityHigh correlation
total sulfur dioxide is highly correlated with free sulfur dioxideHigh correlation
pH is highly correlated with fixed acidity and 1 other fieldsHigh correlation
density is highly correlated with fixed acidity and 2 other fieldsHigh correlation
fixed acidity is highly correlated with pH and 2 other fieldsHigh correlation
volatile acidity is highly correlated with citric acidHigh correlation
alcohol is highly correlated with densityHigh correlation
free sulfur dioxide is highly correlated with total sulfur dioxideHigh correlation
residual sugar is highly correlated with densityHigh correlation
citric acid is highly correlated with pH and 2 other fieldsHigh correlation
citric acid has 124 (8.0%) zeros Zeros

Reproduction

Analysis started2021-08-22 13:15:25.103390
Analysis finished2021-08-22 13:17:23.838464
Duration1 minute and 58.74 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

fixed acidity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct87
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.125608126
Minimum0.9924381114
Maximum1.2510185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2021-08-22T18:47:24.335859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.9924381114
5-th percentile1.054038083
Q11.092298335
median1.120086909
Q31.156454728
95-th percentile1.205981701
Maximum1.2510185
Range0.2585803887
Interquartile range (IQR)0.06415639312

Descriptive statistics

Standard deviation0.04602346171
Coefficient of variation (CV)0.04088764168
Kurtosis-0.2804046955
Mean1.125608126
Median Absolute Deviation (MAD)0.03134674595
Skewness0.1385741113
Sum1745.818203
Variance0.002118159028
MonotonicityNot monotonic
2021-08-22T18:47:25.892845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0960524467
 
4.3%
1.09229833557
 
3.7%
1.1168745953
 
3.4%
1.10681099851
 
3.3%
1.08845495150
 
3.2%
1.11359280749
 
3.2%
1.12932978545
 
2.9%
1.08048638545
 
2.9%
1.11023912845
 
2.9%
1.10330573944
 
2.8%
Other values (77)1045
67.4%
ValueCountFrequency (%)
0.99243811142
 
0.1%
0.9989288934
 
0.3%
1.0052146772
 
0.1%
1.0113056254
 
0.3%
1.02294032214
0.9%
1.0285012171
 
0.1%
1.0339016674
 
0.3%
1.039148959
0.6%
1.04424989813
0.8%
1.04921092916
1.0%
ValueCountFrequency (%)
1.25101852
0.1%
1.2430762051
 
0.1%
1.2394705341
 
0.1%
1.2369942911
 
0.1%
1.2357335872
0.1%
1.2318575491
 
0.1%
1.2305331833
0.2%
1.2291920983
0.2%
1.2264583953
0.2%
1.2250650682
0.1%

volatile acidity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct140
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5263120567
Minimum0.12
Maximum1.33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2021-08-22T18:47:26.598909image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.12
5-th percentile0.27
Q10.39
median0.52
Q30.635
95-th percentile0.84
Maximum1.33
Range1.21
Interquartile range (IQR)0.245

Descriptive statistics

Standard deviation0.1755455183
Coefficient of variation (CV)0.3335388504
Kurtosis0.6144300092
Mean0.5263120567
Median Absolute Deviation (MAD)0.12
Skewness0.5557783182
Sum816.31
Variance0.03081622901
MonotonicityNot monotonic
2021-08-22T18:47:27.785500image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.646
 
3.0%
0.544
 
2.8%
0.4343
 
2.8%
0.5938
 
2.5%
0.5838
 
2.5%
0.3637
 
2.4%
0.436
 
2.3%
0.3935
 
2.3%
0.3834
 
2.2%
0.5634
 
2.2%
Other values (130)1166
75.2%
ValueCountFrequency (%)
0.123
 
0.2%
0.162
 
0.1%
0.188
0.5%
0.192
 
0.1%
0.23
 
0.2%
0.216
0.4%
0.226
0.4%
0.235
 
0.3%
0.2413
0.8%
0.257
0.5%
ValueCountFrequency (%)
1.332
0.1%
1.241
 
0.1%
1.1851
 
0.1%
1.181
 
0.1%
1.1151
 
0.1%
1.091
 
0.1%
1.042
0.1%
1.0351
 
0.1%
1.0251
 
0.1%
1.023
0.2%

citric acid
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct78
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2705738233
Minimum0
Maximum0.78
Zeros124
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2021-08-22T18:47:28.893498image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1
median0.26
Q30.42
95-th percentile0.6
Maximum0.78
Range0.78
Interquartile range (IQR)0.32

Descriptive statistics

Standard deviation0.1924782171
Coefficient of variation (CV)0.7113704302
Kurtosis-0.8911411064
Mean0.2705738233
Median Absolute Deviation (MAD)0.16
Skewness0.2818176003
Sum419.66
Variance0.03704786406
MonotonicityNot monotonic
2021-08-22T18:47:29.972291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0124
 
8.0%
0.4965
 
4.2%
0.2448
 
3.1%
0.0247
 
3.0%
0.2638
 
2.5%
0.135
 
2.3%
0.2133
 
2.1%
0.0833
 
2.1%
0.3232
 
2.1%
0.0132
 
2.1%
Other values (68)1064
68.6%
ValueCountFrequency (%)
0124
8.0%
0.0132
 
2.1%
0.0247
 
3.0%
0.0329
 
1.9%
0.0428
 
1.8%
0.0520
 
1.3%
0.0624
 
1.5%
0.0722
 
1.4%
0.0833
 
2.1%
0.0928
 
1.8%
ValueCountFrequency (%)
0.781
 
0.1%
0.762
 
0.1%
0.751
 
0.1%
0.744
0.3%
0.733
 
0.2%
0.721
 
0.1%
0.711
 
0.1%
0.72
 
0.1%
0.694
0.3%
0.688
0.5%

residual sugar
Real number (ℝ≥0)

HIGH CORRELATION

Distinct85
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5130771125
Minimum0.1640382198
Maximum0.809290928
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2021-08-22T18:47:30.968096image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.1640382198
5-th percentile0.3607363954
Q10.4500494896
median0.5131574298
Q30.5729387001
95-th percentile0.7073442049
Maximum0.809290928
Range0.6452527082
Interquartile range (IQR)0.1228892104

Descriptive statistics

Standard deviation0.1038069158
Coefficient of variation (CV)0.2023222499
Kurtosis0.5064301426
Mean0.5130771125
Median Absolute Deviation (MAD)0.0631079402
Skewness0.1026886922
Sum795.7826015
Variance0.01077587576
MonotonicityNot monotonic
2021-08-22T18:47:31.872204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4733815895154
 
9.9%
0.5131574298128
 
8.3%
0.49430184125
 
8.1%
0.4238773323124
 
8.0%
0.4500494896117
 
7.5%
0.5302327009108
 
7.0%
0.545762660884
 
5.4%
0.559943167284
 
5.4%
0.572938700179
 
5.1%
0.394331075674
 
4.8%
Other values (75)474
30.6%
ValueCountFrequency (%)
0.16403821985
 
0.3%
0.22562765234
 
0.3%
0.277689078134
 
2.2%
0.322229961929
 
1.9%
0.360736395456
3.6%
0.37808862092
 
0.1%
0.394331075674
4.8%
0.40956433682
 
0.1%
0.4238773323124
8.0%
0.4500494896117
7.5%
ValueCountFrequency (%)
0.8092909281
0.1%
0.80761538871
0.1%
0.80579776351
0.1%
0.7955712351
0.1%
0.7838718141
0.1%
0.78303097941
0.1%
0.78216929052
0.1%
0.780380261
0.1%
0.7775199731
0.1%
0.77548446512
0.1%

chlorides
Real number (ℝ≥0)

Distinct142
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08644487427
Minimum0.038
Maximum0.611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2021-08-22T18:47:33.161689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.038
5-th percentile0.0555
Q10.071
median0.079
Q30.09
95-th percentile0.122
Maximum0.611
Range0.573
Interquartile range (IQR)0.019

Descriptive statistics

Standard deviation0.04278465987
Coefficient of variation (CV)0.4949357638
Kurtosis46.14881628
Mean0.08644487427
Median Absolute Deviation (MAD)0.009
Skewness5.977871288
Sum134.076
Variance0.00183052712
MonotonicityNot monotonic
2021-08-22T18:47:34.184091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0866
 
4.3%
0.07455
 
3.5%
0.07851
 
3.3%
0.07651
 
3.3%
0.08449
 
3.2%
0.07747
 
3.0%
0.08246
 
3.0%
0.07545
 
2.9%
0.07145
 
2.9%
0.07943
 
2.8%
Other values (132)1053
67.9%
ValueCountFrequency (%)
0.0382
0.1%
0.0394
0.3%
0.0412
0.1%
0.0423
0.2%
0.0431
 
0.1%
0.0443
0.2%
0.0453
0.2%
0.0463
0.2%
0.0474
0.3%
0.0484
0.3%
ValueCountFrequency (%)
0.6111
 
0.1%
0.4671
 
0.1%
0.4641
 
0.1%
0.4221
 
0.1%
0.4153
0.2%
0.4141
 
0.1%
0.4131
 
0.1%
0.4031
 
0.1%
0.4011
 
0.1%
0.3871
 
0.1%

free sulfur dioxide
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.105653517
Minimum0
Maximum5.926269153
Zeros3
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2021-08-22T18:47:34.897503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.535539362
Q12.248370806
median3.10733293
Q33.827880953
95-th percentile4.628351204
Maximum5.926269153
Range5.926269153
Interquartile range (IQR)1.579510147

Descriptive statistics

Standard deviation0.9747761001
Coefficient of variation (CV)0.313871491
Kurtosis-0.6438567568
Mean3.105653517
Median Absolute Deviation (MAD)0.7931477906
Skewness-0.04569342
Sum4816.868605
Variance0.9501884453
MonotonicityNot monotonic
2021-08-22T18:47:35.597527image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.046205856136
 
8.8%
1.812859058103
 
6.6%
3.31711594677
 
5.0%
2.99187742675
 
4.8%
2.7338442274
 
4.8%
2.24837080670
 
4.5%
2.58781360762
 
4.0%
2.86788176559
 
3.8%
3.5042074859
 
3.8%
3.1073329355
 
3.5%
Other values (46)781
50.4%
ValueCountFrequency (%)
03
 
0.2%
0.72919774961
 
0.1%
1.19100804549
 
3.2%
1.53553936241
 
2.6%
1.812859058103
6.6%
1.9340730381
 
0.1%
2.046205856136
8.8%
2.24837080670
4.5%
2.42718610155
3.5%
2.58781360762
4.0%
ValueCountFrequency (%)
5.9262691531
 
0.1%
5.4990750751
 
0.1%
5.4022526341
 
0.1%
5.3689554391
 
0.1%
5.335116743
0.2%
5.3007170594
0.3%
5.2657358192
0.1%
5.1939403262
0.1%
5.1570786051
 
0.1%
5.1195401691
 
0.1%

total sulfur dioxide
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct142
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.943582802
Minimum1.875722899
Maximum5.828287448
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2021-08-22T18:47:36.421688image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.875722899
5-th percentile2.549852854
Q13.346596028
median3.962830575
Q34.550629496
95-th percentile5.325481222
Maximum5.828287448
Range3.952564549
Interquartile range (IQR)1.204033468

Descriptive statistics

Standard deviation0.838462075
Coefficient of variation (CV)0.2126142945
Kurtosis-0.7006047324
Mean3.943582802
Median Absolute Deviation (MAD)0.6162345468
Skewness-0.005040227028
Sum6116.496925
Variance0.7030186512
MonotonicityNot monotonic
2021-08-22T18:47:37.566412image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.63043713743
 
2.8%
3.44860579536
 
2.3%
3.1130449635
 
2.3%
2.90290132235
 
2.3%
2.82388579633
 
2.1%
3.2353725533
 
2.1%
3.75127865932
 
2.1%
3.99488218131
 
2.0%
3.58741073730
 
1.9%
3.39865379930
 
1.9%
Other values (132)1213
78.2%
ValueCountFrequency (%)
1.8757228993
 
0.2%
2.0452049454
 
0.3%
2.19309218114
 
0.9%
2.32437217613
 
0.8%
2.44247271827
1.7%
2.54985285426
1.7%
2.6483376629
1.9%
2.73931962428
1.8%
2.82388579633
2.1%
2.90290132235
2.3%
ValueCountFrequency (%)
5.8282874481
 
0.1%
5.7884479111
 
0.1%
5.7474085571
 
0.1%
5.7306399461
 
0.1%
5.7221774571
 
0.1%
5.7136619532
0.1%
5.6964691381
 
0.1%
5.6877904132
0.1%
5.6790558443
0.2%
5.6614161853
0.2%

density
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct416
Distinct (%)26.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9967721599
Minimum0.9912
Maximum1.00289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2021-08-22T18:47:38.353536image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.9912
5-th percentile0.993855
Q10.99565
median0.99676
Q30.99781
95-th percentile0.9998
Maximum1.00289
Range0.01169
Interquartile range (IQR)0.00216

Descriptive statistics

Standard deviation0.001729584635
Coefficient of variation (CV)0.001735185536
Kurtosis0.2683798665
Mean0.9967721599
Median Absolute Deviation (MAD)0.0011
Skewness0.1123607415
Sum1545.99362
Variance2.991463009 × 10-6
MonotonicityNot monotonic
2021-08-22T18:47:39.084015image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.997236
 
2.3%
0.996835
 
2.3%
0.997634
 
2.2%
0.99829
 
1.9%
0.996227
 
1.7%
0.997826
 
1.7%
0.99724
 
1.5%
0.998223
 
1.5%
0.996423
 
1.5%
0.996623
 
1.5%
Other values (406)1271
81.9%
ValueCountFrequency (%)
0.99121
 
0.1%
0.99151
 
0.1%
0.991621
 
0.1%
0.991911
 
0.1%
0.99222
0.1%
0.992351
 
0.1%
0.992361
 
0.1%
0.99243
0.2%
0.992422
0.1%
0.992521
 
0.1%
ValueCountFrequency (%)
1.002891
 
0.1%
1.00261
 
0.1%
1.00222
 
0.1%
1.00212
 
0.1%
1.00146
0.4%
1.0016
0.4%
1.00083
 
0.2%
1.00066
0.4%
1.00049
0.6%
1.00032
 
0.1%

pH
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct79
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.310399742
Minimum2.86
Maximum3.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2021-08-22T18:47:40.184982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2.86
5-th percentile3.07
Q13.21
median3.31
Q33.4
95-th percentile3.56
Maximum3.78
Range0.92
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation0.1443163017
Coefficient of variation (CV)0.04359482629
Kurtosis0.07326044267
Mean3.310399742
Median Absolute Deviation (MAD)0.09
Skewness0.06090359956
Sum5134.43
Variance0.02082719494
MonotonicityNot monotonic
2021-08-22T18:47:41.436073image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.3656
 
3.6%
3.356
 
3.6%
3.2651
 
3.3%
3.3848
 
3.1%
3.3948
 
3.1%
3.2946
 
3.0%
3.3244
 
2.8%
3.3443
 
2.8%
3.2842
 
2.7%
3.3139
 
2.5%
Other values (69)1078
69.5%
ValueCountFrequency (%)
2.861
 
0.1%
2.882
 
0.1%
2.892
 
0.1%
2.921
 
0.1%
2.933
0.2%
2.944
0.3%
2.984
0.3%
2.992
 
0.1%
35
0.3%
3.013
0.2%
ValueCountFrequency (%)
3.782
 
0.1%
3.722
 
0.1%
3.713
 
0.2%
3.694
0.3%
3.682
 
0.1%
3.673
 
0.2%
3.664
0.3%
3.633
 
0.2%
3.624
0.3%
3.618
0.5%

sulphates
Real number (ℝ)

Distinct92
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.6117173117
Minimum-2.114887349
Maximum0.4857661269
Zeros1
Zeros (%)0.1%
Negative1497
Negative (%)96.5%
Memory size12.2 KiB
2021-08-22T18:47:42.183568image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.114887349
5-th percentile-1.157869371
Q1-0.8350811533
median-0.6228176745
Q3-0.3736894824
95-th percentile-0.07544061908
Maximum0.4857661269
Range2.600653476
Interquartile range (IQR)0.4613916709

Descriptive statistics

Standard deviation0.3348132031
Coefficient of variation (CV)-0.5473332154
Kurtosis0.3393083807
Mean-0.6117173117
Median Absolute Deviation (MAD)0.2122634787
Skewness-0.02792970866
Sum-948.7735505
Variance0.112099881
MonotonicityNot monotonic
2021-08-22T18:47:42.975084image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.737641838568
 
4.4%
-0.67825667368
 
4.4%
-0.870043809968
 
4.4%
-0.622817674559
 
3.8%
-0.801405188958
 
3.7%
-0.768946833254
 
3.5%
-0.835081153350
 
3.2%
-0.906367539949
 
3.2%
-0.70743037849
 
3.2%
-0.596458203548
 
3.1%
Other values (82)980
63.2%
ValueCountFrequency (%)
-2.1148873491
 
0.1%
-1.7651730642
 
0.1%
-1.6179900774
 
0.3%
-1.5500831923
 
0.2%
-1.4242589055
 
0.3%
-1.3658677328
0.5%
-1.31021110914
0.9%
-1.25710309112
0.8%
-1.20637414218
1.2%
-1.15786937119
1.2%
ValueCountFrequency (%)
0.48576612691
0.1%
0.4783150852
0.1%
0.37749591931
0.1%
0.35442731941
0.1%
0.26232430042
0.1%
0.25155223061
0.1%
0.23475786961
0.1%
0.21715126172
0.1%
0.20493301361
0.1%
0.17925639211
0.1%

alcohol
Real number (ℝ≥0)

HIGH CORRELATION

Distinct62
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.39115624
Minimum8.4
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2021-08-22T18:47:43.698946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum8.4
5-th percentile9.2
Q19.5
median10.1
Q311
95-th percentile12.4
Maximum14
Range5.6
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.01453631
Coefficient of variation (CV)0.09763459292
Kurtosis-0.1858881517
Mean10.39115624
Median Absolute Deviation (MAD)0.7
Skewness0.7584740148
Sum16116.68333
Variance1.029283924
MonotonicityNot monotonic
2021-08-22T18:47:44.401567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.5136
 
8.8%
9.4102
 
6.6%
9.876
 
4.9%
9.269
 
4.4%
1067
 
4.3%
10.566
 
4.3%
1159
 
3.8%
9.359
 
3.8%
9.659
 
3.8%
9.754
 
3.5%
Other values (52)804
51.8%
ValueCountFrequency (%)
8.42
 
0.1%
8.51
 
0.1%
8.72
 
0.1%
927
 
1.7%
9.051
 
0.1%
9.122
 
1.4%
9.269
4.4%
9.2333333331
 
0.1%
9.251
 
0.1%
9.359
3.8%
ValueCountFrequency (%)
141
 
0.1%
13.61
 
0.1%
13.51
 
0.1%
13.43
 
0.2%
13.33
 
0.2%
13.21
 
0.1%
13.11
 
0.1%
135
 
0.3%
12.98
0.5%
12.816
1.0%

quality
Real number (ℝ≥0)

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6344294
Minimum3
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2021-08-22T18:47:45.300151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum8
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7958446396
Coefficient of variation (CV)0.1412467143
Kurtosis0.2488314966
Mean5.6344294
Median Absolute Deviation (MAD)1
Skewness0.2504714688
Sum8739
Variance0.6333686903
MonotonicityNot monotonic
2021-08-22T18:47:45.825881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5667
43.0%
6621
40.0%
7190
 
12.3%
449
 
3.2%
816
 
1.0%
38
 
0.5%
ValueCountFrequency (%)
38
 
0.5%
449
 
3.2%
5667
43.0%
6621
40.0%
7190
 
12.3%
816
 
1.0%
ValueCountFrequency (%)
816
 
1.0%
7190
 
12.3%
6621
40.0%
5667
43.0%
449
 
3.2%
38
 
0.5%

Interactions

2021-08-22T18:45:30.592960image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:31.306931image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:31.922135image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:32.794372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:33.495059image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:34.112762image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:34.806875image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:35.632893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:36.296422image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:36.951496image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:37.571604image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:38.155624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:38.774675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:39.415503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:40.056823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:40.765800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:41.445838image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:42.096403image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:42.931664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:43.608098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:44.273951image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:45.075388image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:45.733552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:46.354520image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:46.959118image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:47.784424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:48.516109image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:49.325906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:49.976276image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:50.732051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:51.497966image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:52.527065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:53.358788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:54.533046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:55.507900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:56.327555image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:57.546984image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:58.446444image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:45:59.577720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:00.351989image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:01.305094image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:02.297668image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:03.088104image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:03.964870image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:04.639656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:05.415175image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:06.186420image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:06.914083image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:07.620265image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:08.241295image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:08.868828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:09.492970image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:10.037313image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:10.436207image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:10.886859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:11.711709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:12.238774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:12.736094image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:13.207924image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:13.742239image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:14.391832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:15.107885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:15.728029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:16.746871image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:17.399112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:18.471743image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:19.458861image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:20.298540image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:20.963655image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:21.570858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:22.546508image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:23.218413image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:23.871699image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:24.904210image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:25.745030image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:27.027077image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:27.839460image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:28.624573image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:29.668421image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:30.587167image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:31.811408image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:32.679560image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:34.061300image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:34.822666image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:35.541136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:36.140035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:36.779996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:37.433987image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:38.128824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:38.729065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:39.469089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:40.179176image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:40.905741image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:41.534843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:42.124804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:42.803528image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:43.453974image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:44.380254image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:44.934561image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:45.630814image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:46.372609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:47.039424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:47.631608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:48.381168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:49.171795image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:49.730760image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:50.374723image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:50.985200image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:51.663027image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:52.280281image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:52.841401image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:53.536331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:54.501985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:55.684171image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:56.623286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:57.461016image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:58.130924image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:58.818823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:46:59.471022image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:47:00.138116image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:47:00.900057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:47:01.845782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:47:02.853922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:47:04.092834image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:47:05.052702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:47:05.730811image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:47:06.393326image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:47:07.074392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:47:07.861522image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:47:08.701382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:47:09.468668image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:47:10.101696image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:47:10.749151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:47:11.372596image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:47:11.990832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:47:13.085241image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:47:14.080334image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:47:15.144984image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:47:16.386636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:47:17.304224image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:47:18.035733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:47:18.521227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:47:19.221225image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-22T18:47:19.999490image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-08-22T18:47:46.583139image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-22T18:47:48.348474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-22T18:47:49.553654image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-22T18:47:50.916707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-08-22T18:47:21.183269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-08-22T18:47:22.994834image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
01.1033060.700.000.4500490.0762.8678823.8614900.99783.51-0.8014059.45
11.1168750.880.000.5729390.0984.1025214.6868350.99683.20-0.4766309.85
21.1168750.760.040.5302330.0923.3171164.4212900.99703.26-0.5462469.85
31.1982550.280.560.4500490.0753.5042074.5506290.99803.16-0.7376429.86
41.1033060.700.000.4500490.0762.8678823.8614900.99783.51-0.8014059.45
51.1033060.660.000.4238770.0753.1073334.0566520.99783.51-0.8014059.45
61.1200870.600.060.3607360.0693.3171164.5299510.99643.30-1.2063749.45
71.0997210.650.000.1640380.0653.3171163.2922420.99463.39-1.15786910.07
81.1168750.580.020.4733820.0732.5878143.1130450.99683.36-0.7689479.57
91.1068110.500.360.7469710.0713.5042075.2126360.99783.35-0.25180410.55

Last rows

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
15411.0721170.7250.200.7722170.0734.3418484.8916210.997703.29-0.8700449.25
15421.0587370.5500.150.4238770.0774.1652643.8961820.993143.32-0.22091411.66
15431.0113060.7400.090.3943310.0893.4131633.5428440.994023.67-0.80140511.66
15441.0587370.5100.130.5302330.0764.3418484.0566520.995743.42-0.33647011.06
15451.0804860.6200.080.4500490.0684.2847973.9948820.996513.42-0.2209149.56
15461.0540380.6000.080.4733820.0904.5034484.1718560.994903.45-0.73764210.55
15471.0391490.5500.100.5131570.0624.8352654.3514120.995123.52-0.31861811.26
15481.0587370.5100.130.5302330.0764.3418484.0566520.995743.42-0.33647011.06
15491.0391490.6450.120.4733820.0754.5034484.1718560.995473.57-0.41307210.25
15501.0442500.3100.470.6604150.0673.5907834.1155560.995493.39-0.52231611.06

Duplicate rows

Most frequently occurring

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality# duplicates
191.0763540.4600.240.3943310.0773.5907833.8614900.994803.39-0.67825710.664
491.0960520.3600.460.4943020.0744.0375944.1718560.995343.40-0.17738811.074
601.0960520.6950.130.4733820.0762.9918773.2353730.995463.29-0.87004410.154
781.1068110.5100.020.3943310.0843.1073333.7512790.995383.36-0.87004410.564
41.0442500.5000.000.2776890.0573.3171163.5428440.994483.36-1.2571039.553
111.0633130.6400.210.4238770.0813.2154303.7512790.996893.59-0.5223169.853
361.0884550.6500.020.4943020.0662.4271863.4966200.997203.47-0.4991229.563
371.0884550.6900.070.5599430.0913.3171163.2922420.995723.38-0.67825711.363
571.0960520.6300.000.4500490.0973.2154303.9948820.996753.37-0.7376429.063
1011.1168750.6000.260.4733820.0804.4510775.5310520.996223.21-0.9441339.953